Electroencephalographs provide useful data on brain activity that is used for a variety of purposes, such as medical research and clinical treatment. EEG signals, however, often contain ocular artifacts produced by eye activity. These ocular artifacts adversely affect the data collected on brain activity. Consequently, adaptive filter algorithms, based on the Least Mean Square algorithm, have been developed to filter out ocular artifacts from EEG signals.
Adaptive filter algorithms use signals from ocular sensors, in conjunction with signals at EEG sites of interest, to derive an estimate of the noise from eye activity at various EEG sites. Once the adaptive filter algorithm has stabilized, these site specific noise estimates can be subtracted from the associated EEG site to produce cleaner EEG signals.
Current applications of adaptive filter algorithms are typically limited to controlled laboratory environments. In mobile, non-laboratory controlled environments, however, noise spikes in ocular sensor channels are often induced by non-ocular sources such as rubbing and sensor movement. These non-ocular sources can cause prolonged high amplitude spikes which in turn can prevent adaptive filter algorithms from converging within a reasonable period of time. When this occurs, the unstable filter algorithms corrupt the EEG signal and render the algorithms and EEG signals virtually useless.
For the reasons stated above, and for other reasons stated below which will become apparent to those skilled in the art upon reading and understanding the present specification, there is a need in the art for an electroencephalograph which is not susceptible to instability of an adaptive filter algorithm in the presence of prolonged high amplitude noise spikes in ocular sensor channels.